基于本体的网络学习资源推荐算法设计

赵继春; 孙素芬; 郭建鑫; 钟瑶; 王敏; 秦莹

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基于本体的网络学习资源推荐算法设计

更新时间：2026-09-04

- 基于本体的网络学习资源推荐算法设计
- Recommendation algorithm design of network learning resource based on ontology
- 微电子学与计算机 2021年38卷第1期页码：64-69
- 作者机构：
  
  1. 北京市农林科学院机构,北京,100097
  2. 北京市农村远程信息服务工程技术研究中心机构,北京,100097
- 作者简介：
- 基金信息：
  
  北京市自然科学基金 9172009北京市科技计划项目 Z191100007519010北京市农业科技项目 20200127
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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赵继春, 孙素芬, 郭建鑫, 钟瑶, 王敏, 秦莹. 基于本体的网络学习资源推荐算法设计[J]. 微电子学与计算机, 2021, 38(1): 64-69.

ZHAO Ji-chun, SUN Su-fen, GUO Jian-xin, et al. Recommendation algorithm design of network learning resource based on ontology[J]. 2021, 38(1): 64-69.
赵继春, 孙素芬, 郭建鑫, 钟瑶, 王敏, 秦莹. 基于本体的网络学习资源推荐算法设计[J]. 微电子学与计算机, 2021, 38(1): 64-69. DOI：

ZHAO Ji-chun, SUN Su-fen, GUO Jian-xin, et al. Recommendation algorithm design of network learning resource based on ontology[J]. 2021, 38(1): 64-69. DOI：

摘要

针对学习者在网络学习平台中面临的信息过载导致获取个性化信息不足问题，应用领域本体构建方法，对学习课件进行数据建模及表示，构建了一种基于领域本体与学习者属性信息的特征模型，模型结合领域本体主题及学习者基本信息要素具有语义支持并随时间推移进行更新.以学习特征模型为基础设计一种融合相似度的协同过滤推荐方法，有效缓解推荐算法面临的冷启动及其语义支持不足的问题.实验结果表明，所提的方法与传统的协同过滤推荐方法相比，数据推荐的准确率MAE减小12.29%，有效提高数据推荐质量.

Abstract

In view of the problem that learners are faced with information overload in the network learning platform

which leads to insufficient access to personalized information

this paper applies the domain ontology construction method to model and represent the data of learning courseware

and constructs a learner feature model based on domain ontology and learner attribute information. The model has semantic support combined with domain ontology theme and basic information elements of learners and updates with time. Based on the learning feature model

a collaborative filtering recommendation method is designed to integrate similarity

which can effectively solve the problem of cold start and lack of semantic support. The experimental results show that the data recommendation precision of proposed method is better than the traditional collaborative filtering recommendation method

the accuracy of data recommendation decreased by 12.29%

effectively improving the quality of data recommendation.

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references

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